Support journalism that doesn't flinch . Support journalism that doesn't flinch . Elon Musk leaves a meeting with House Republicans in the basement of the US Capitol building on March 5, 2025 in Washington, DC. Get your news from a source that's not owned and controlled by oligarchs. Tennessee teenagers are suing Elon Musk's company xAI over allegations that its artificial intelligence tool Grok undressed photos of them as minors--the latest challenge against the wealthiest living person's chatbot .

Minnesota Is Just the Beginning. California and New York Are'Next' The Trump administration appears to be planning to leverage the same playbook used in Minnesota to go after other blue states. The Trump administration appears to be deploying the same playbook it used in Minnesota --leveraging allegations of fraud to justify significant federal oversight --in other blue states across the country, starting with California and New York. "POTUS loves Minnesota and the people. It's a state where he received historic Republican support, and he has long called out [Governor Tim] Walz for his incompetence and terrible leadership," a senior White House official tells WIRED.

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Garrett Butts details military drone innovation effort aimed at speeding deployment and reducing cost in an exclusive interview with Fox News Digital. As the war between Israel and Iran intensifies, one Army drone expert is warning that the U.S. must stay ready, and fast. Garrett Butts is helping lead the charge by building smarter, cheaper unmanned aircraft systems (UAS) in-house for the battlefield. In an exclusive interview with Fox News Digital on Tuesday, Butts described how his team is creating drone technology from scratch, often using parts it took nearly a year to legally obtain. "We're a transformation and contact unit," said Butts, who serves with the 1st Cavalry Division.

Texas Rep. Jasmine Crockett attacked President Donald Trump's West Point address on MSNBC and called it proof of his unfitness as commander in chief. Rep. Jasmine Crockett, D-Texas, appears to be leaning in on her rising political stardom this week, briefly sharing what appeared to be a fan-made song that referred to the Democratic firebrand as the "leader of the future." "Jasmine Crockett, she rises with the dawn. Fighting for justice, her light will never be gone," the song went. Infectious with passion, she'll never bow down.

Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks -- from understanding the relationships between biological entities to developing therapeutics. However, these data encompass diverse perturbations and readouts, and the complex dependence of experimental outcomes on their biological context makes it challenging to integrate insights across experiments. Here, we present the Large Perturbation Model (LPM), a deep-learning model that integrates multiple, heterogeneous perturbation experiments by representing perturbation, readout, and context as disentangled dimensions. LPM outperforms existing methods across multiple biological discovery tasks, including in predicting post-perturbation transcriptomes of unseen experiments, identifying shared molecular mechanisms of action between chemical and genetic perturbations, and facilitating the inference of gene-gene interaction networks.

High Content Screening (HCS) microscopy datasets have transformed the ability to profile cellular responses to genetic and chemical perturbations, enabling cell-based inference of drug-target interactions (DTI). However, the adoption of representation learning methods for HCS data has been hindered by the lack of accessible datasets and robust benchmarks. To address this gap, we present RxRx3-core, a curated and compressed subset of the RxRx3 dataset, and an associated DTI benchmarking task. At just 18GB, RxRx3-core significantly reduces the size barrier associated with large-scale HCS datasets while preserving critical data necessary for benchmarking representation learning models against a zero-shot DTI prediction task. RxRx3-core includes 222,601 microscopy images spanning 736 CRISPR knockouts and 1,674 compounds at 8 concentrations. RxRx3-core is available on HuggingFace and Polaris, along with pre-trained embeddings and benchmarking code, ensuring accessibility for the research community. By providing a compact dataset and robust benchmarks, we aim to accelerate innovation in representation learning methods for HCS data and support the discovery of novel biological insights.

High-content perturbation experiments allow scientists to probe biomolecular systems at unprecedented resolution, but experimental and analysis costs pose significant barriers to widespread adoption. Machine learning has the potential to guide efficient exploration of the perturbation space and extract novel insights from these data. However, current approaches neglect the semantic richness of the relevant biology, and their objectives are misaligned with downstream biological analyses. In this paper, we hypothesize that large language models (LLMs) present a natural medium for representing complex biological relationships and rationalizing experimental outcomes. We propose PerturbQA, a benchmark for structured reasoning over perturbation experiments. Unlike current benchmarks that primarily interrogate existing knowledge, PerturbQA is inspired by open problems in perturbation modeling: prediction of differential expression and change of direction for unseen perturbations, and gene set enrichment. We evaluate state-of-the-art machine learning and statistical approaches for modeling perturbations, as well as standard LLM reasoning strategies, and we find that current methods perform poorly on PerturbQA. As a proof of feasibility, we introduce Summer (SUMMarize, retrievE, and answeR, a simple, domain-informed LLM framework that matches or exceeds the current state-of-the-art. Our code and data are publicly available at https://github.com/genentech/PerturbQA.

Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it difficult to model the phenomena of interest. We address this by learning two embeddings to independently represent the phenomena of interest and the spurious correlations. The embedding representing the phenomena of interest is correlated with the target variable $y$, and is invariant to the environment variable $e$. In contrast, the embedding representing the spurious correlations is correlated with $e$. The invariance to $e$ is difficult to achieve on real-world datasets. Our primary contribution is an algorithm called Supervised Contrastive Block Disentanglement (SCBD) that effectively enforces this invariance. It is based purely on Supervised Contrastive Learning, and applies to real-world data better than existing approaches. We empirically validate SCBD on two challenging problems. The first problem is domain generalization, where we achieve strong performance on a synthetic dataset, as well as on Camelyon17-WILDS. We introduce a single hyperparameter $\alpha$ to control the degree of invariance to $e$. When we increase $\alpha$ to strengthen the degree of invariance, out-of-distribution performance improves at the expense of in-distribution performance. The second problem is batch correction, in which we apply SCBD to preserve biological signal and remove inter-well batch effects when modeling single-cell perturbations from 26 million Optical Pooled Screening images.

Fox News host Laura Ingraham gives her take on the spending freeze on USAID on'The Ingraham Angle.' Tesla and Space X CEO Elon Musk's DOGE efforts to slash government waste and streamline the federal bureaucracy include the hiring of several up-and-coming young software engineers tasked with "modernizing federal technology and software to maximize governmental efficiency and productivity." Six young men between the ages of 19 and 24 -- Akash Bobba, Edward Coristine, Luke Farritor, Gautier Cole Killian, Gavin Kliger and Ethan Shaotran -- have taken up various roles furthering the DOGE agenda, according to a report from Wired. Bobba was part of the highly regarded Management, Entrepreneurship, and Technology program at UC Berkeley and has held internships at the Bridgewater Associates hedge fund, Meta and Palantir. "Let me tell you something about Akash," Grata AI CEO Charis Zhang posted on X about Bobba in recent days. "During a project at Berkeley, I accidentally deleted our entire codebase 2 days before the deadline. Akash just stared at the screen, shrugged, and rewrote everything from scratch in one night -- better than before. We submitted early and got first in the class. I trust him with everything I own."